Considerations of AI-powered Autonomic Service Agent Communication
draft-han-anima-ai-asa-01

ANIMA                                                        M. Han, Ed.
Internet-Draft                                             N. Zhang, Ed.
Intended status: Standards Track                            J. Zhao, Ed.
Expires: 20 July 2026                                       China Unicom
                                                         16 January 2026

   Considerations of AI-powered Autonomic Service Agent Communication
                       draft-han-anima-ai-asa-01

Abstract

   ANIMA defined Autonomic Service Agent to build intelligent management
   functions into network devices, and could interact with each other
   through a standard protocol (aka GRASP).With the rapid advancement of
   Large Language Model (LLM)-driven AI technologies, there is now a
   potential opportunity to enhance the ASA to be AI-powered, thereby
   elevating the intelligence of device-built-in management functions to
   a whole new level.This document analyzes the impact of the AI-powered
   ASA, mostly from the perspective of the ASA communication protocol.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   3
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Definition of ASA . . . . . . . . . . . . . . . . . . . .   3
     3.2.  Emergence of AI-powered Agent . . . . . . . . . . . . . .   4
   4.  The Vision of AI-powered ASA  . . . . . . . . . . . . . . . .   4
   5.  Scenarios of AI-powered ASA Communication between Network
           Devices . . . . . . . . . . . . . . . . . . . . . . . . .   4
     5.1.  General . . . . . . . . . . . . . . . . . . . . . . . . .   5
     5.2.  Possible Examples . . . . . . . . . . . . . . . . . . . .   5
       5.2.1.  AI Agent based Router for Automatic Congestion
               Relief  . . . . . . . . . . . . . . . . . . . . . . .   5
       5.2.2.  AI Agent based Router for Automatic Network DDoS
               Attacks Defense . . . . . . . . . . . . . . . . . . .   5
   6.  Scenarios of AI-powered ASA Communication between Network
           Management Systems and Devices  . . . . . . . . . . . . .   5
     6.1.  General . . . . . . . . . . . . . . . . . . . . . . . . .   6
     6.2.  Possible Examples . . . . . . . . . . . . . . . . . . . .   6
       6.2.1.  Coordinated IPv6 Monitoring . . . . . . . . . . . . .   6
   7.  Potential New Requirements of GRASP . . . . . . . . . . . . .   6
     7.1.  The interface and model extension for Prompt with AI
           agent . . . . . . . . . . . . . . . . . . . . . . . . . .   7
     7.2.  Defination of Option for AI-ASA . . . . . . . . . . . . .   7
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     10.1.  Normative References . . . . . . . . . . . . . . . . . .   7
     10.2.  Informative References . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   The ANIMA provides a vision of a network that configures, heals,
   optimizes, and protects itself.  An ASA is defined in [RFC7575] as
   "An agent implemented on an autonomic node that implements an
   autonomic function, either in part (in the case of a distributed
   function) or whole.

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   [RFC9222] proposes guidelines for the design of Autonomic Service
   Agents for autonomic networks.  Autonomic Service Agents, together
   with the Autonomic Network Infrastructure, the Autonomic Control
   Plane, and the GeneRic Autonomic Signaling Protocol, constitute the
   base elements of an autonomic networking ecosystem.

   Large-scale network models have attracted much attention in the field
   of artificial intelligence in recent years.  They integrate the
   advantages of network technology and LLMs and show great potential in
   many fields.  Especially for network operation and maintenance, it is
   demonstrating huge enabling potential and providing innovative
   approaches to solve increasingly complex network operation and
   maintenance problems.

   AI-ASA can achieve more intelligent management functions.  Embedding
   AI-ASA into network devices can enhance operation and maintenance
   efficiency with LLMs.

   This draft analyzes AI-ASA vision and potential functions and
   describes the scenarios of AI-powered ASA Communication between
   Network Devices and Network Management Systems.  The potential new
   requirements of GRASP are also discussed.

2.  Conventions and Definitions

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Background

3.1.  Definition of ASA

   In [RFC8993], ASA is a process that makes use of the features
   provided by the ANI to achieve its own goals, usually including
   interaction with other ASAs via GRASP [RFC8990] or otherwise.  Of
   course, it also interacts with the specific targets of its function,
   using any suitable mechanism.  Unless its function is very simple,
   the ASA will need to handle overlapping asynchronous operations.  It
   may therefore be a quite complex piece of software in its own right,
   forming part of the application layer above the ANI.

   Autonomic Service Agents, together with the Autonomic Network
   Infrastructure, the Autonomic Control Plane, and the GeneRic
   Autonomic Signaling Protocol, constitute the base elements of an
   autonomic networking ecosystem.

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3.2.  Emergence of AI-powered Agent

   [I-D.rosenberg-ai-protocols] Intelligent agent, as an important
   concept in the field of artificial intelligence, refers to a system
   that can autonomously perceive the environment, make decisions, and
   execute actions.  It has basic characteristics such as autonomy,
   interactivity, reactivity, and adaptability, and can independently
   complete tasks in complex and changing environments.  Intelligent
   agents can learn and make decisions.

   [I-D.chuyi-nmrg-ai-agent-network] Al Agent, an automated intelligent
   entity capable of interacting with its environment, acquiring
   contextual informationreasoning, self-learning, decision-making,
   executing tasks (autonomously or in collaboration with other Al
   Agents) to achieve

   There are a few examples of AI Agents.

   A travel AI Agent that can help users search for travel destinations
   based on preferences, compare flight and hotel costs, make bookings,
   and adjust plans

   A loan handling agent that can help users take out a loan.  The AI
   Agent can access a user's salary information, credit history, and
   then interact with the user to identify the right loan for the target
   use case the customer has in mind

   A shopping agent for clothing that can listen to user preferences and
   interests, look at prior purchases, and show users different options,
   ultimately helping a user find the right sports coat for an event

   AI Agent in 3GPP, an automated intelligent entity capable of
   interacting with its environment, acquiring contextual
   informationreasoning, self-learning, decision-making, executing tasks
   autonomously or in collaboration with other AI Agents to achieve a
   specific goal

4.  The Vision of AI-powered ASA

   The AI-powered ASA provides more intelligent operation and management
   of network devices to achieve the Intention-driven network and Auto-
   driven network.

5.  Scenarios of AI-powered ASA Communication between Network Devices

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5.1.  General

   The network devices to communicate with other network devices through
   anima's interface.

5.2.  Possible Examples

5.2.1.  AI Agent based Router for Automatic Congestion Relief

   In the automatic congestion relief use case, the traditional solution
   relies on built-in intelligent modules in devices to implement
   traffic rerouting via traditional protocols (BGP-LS/BGP-RPD).  Device
   interactions are constrained by predefined protocol rules (e.g.,
   policy triggering based on fixed bandwidth thresholds), lacking
   cross-device historical data sharing and AI model collaboration.
   Policy generation depends solely on local TOP-N traffic modeling,
   unable to adaptively optimize based on real-time traffic patterns.

   When AI-powered Agents are introduced into network devices, AI-
   powered ASA Communication can be established between devices.
   Devices extend BGP-LS to synchronize real-time link bandwidth and
   TOP-N traffic characteristics.  The AI-powered Agents dynamically
   define congestion thresholds based on traffic data, replacing manual
   threshold configuration.  Upon detecting congestion, devices use the
   GRASP protocol to negotiate AI-generated policies (e.g., dynamic
   adjustment of Multi-Exit Discriminator (MED) values) and route
   traffic precisely to lightly loaded links via the BGP Routing Process
   Daemon (BGP RPD).  Reinforcement learning is applied to dynamically
   optimize policy parameters during this process.

5.2.2.  AI Agent based Router for Automatic Network DDoS Attacks Defense

   With the evolution of attack forms, the Distributed Denial of Service
   (DDoS) Attacks present the features of short-term and high-frequency
   outbreaks, and the attack peak value keeps rising year by year,
   imposing an extreme challenge on the defense response speed.  In
   response to the above attack problems, this document innovatively
   puts forward an edge defense architecture: deploy attack detection
   functions to end devices, achieve second-level flash defense against
   DDoS attacks via intelligent service traffic monitoring, and
   establish an autonomous network DDoS attack defense system.  In the
   meantime, rely on the AI Agent based Router to support the second-
   level discovery and real-time interception of attack behaviors, so as
   to strengthen the network security barrier.

6.  Scenarios of AI-powered ASA Communication between Network Management
    Systems and Devices

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6.1.  General

   The network controller communicates with other netwok devices by the
   anima interface or protocol.

6.2.  Possible Examples

6.2.1.  Coordinated IPv6 Monitoring

   In the current IPv6 end-to-end traffic monitoring scenario, traffic
   data collection and analysis rely on manual intervention, while the
   large volume of live network traffic data results in high resource
   requirements.  When AI-powered Agents are deployed in network
   controllers and devices, AI-powered ASA communication can be
   established between IDC controllers and edge devices to enable
   hierarchical collaboration.

   The controller's AI-powered Agent module discovers network devices
   via the GRASP protocol, initiates multi-threaded real-time collection
   and monitoring of IPv6/IP traffic data, and performs preliminary
   analysis including flow pattern recognition and IPv6/IPv4 traffic
   ratio trending.  Concurrently, the device-side AI-powered Agent
   collects customer traffic data, decomposes traffic distribution
   characteristics to identify high-value business scenarios, and
   synchronizes these insights to the controller via the GRASP protocol.
   The controller's AI-powered Agent integrates provincial-level traffic
   ingress/egress data to construct regional traffic matrices and
   uploads preliminary analysis results (e.g., internal IDC traffic
   distribution, inter-provincial link utilization) to the IPv6 end-to-
   end monitoring platform.

   The IPv6 end-to-end monitoring platform leverages multi-dimensional
   data models to conduct in-depth analysis on the uploaded traffic data
   and preliminary results, generating final operational decisions such
   as inter-provincial link bandwidth expansion plans and CDN node
   deployment recommendations.  These decisions are then disseminated to
   the controller, which issues configuration instructions to the
   device-side AI-powered Agents via the GRASP API.  Upon receiving the
   instructions, the device's intelligent module invokes relevant
   interfaces to adjust server resources and verifies operational
   effectiveness through self-monitoring threads.

7.  Potential New Requirements of GRASP

   TBD

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7.1.  The interface and model extension for Prompt with AI agent

   TBD

7.2.  Defination of Option for AI-ASA

   TBD

8.  Security Considerations

   Uncertainty of Current AI Technologies.

9.  IANA Considerations

   TBD

10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

10.2.  Informative References

   [RFC8993]  Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia,
              L., and J. Nobre, "A Reference Model for Autonomic
              Networking", RFC 8993, DOI 10.17487/RFC8993, May 2021,
              <https://www.rfc-editor.org/info/rfc8993>.

   [RFC7575]  Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
              Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
              Networking: Definitions and Design Goals", RFC 7575,
              DOI 10.17487/RFC7575, June 2015,
              <https://www.rfc-editor.org/info/rfc7575>.

   [RFC8990]  Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "GeneRic
              Autonomic Signaling Protocol (GRASP)", RFC 8990,
              DOI 10.17487/RFC8990, May 2021,
              <https://www.rfc-editor.org/info/rfc8990>.

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   [RFC9222]  Carpenter, B., Ciavaglia, L., Jiang, S., and P. Peloso,
              "Guidelines for Autonomic Service Agents", RFC 9222,
              DOI 10.17487/RFC9222, March 2022,
              <https://www.rfc-editor.org/info/rfc9222>.

   [I-D.rosenberg-ai-protocols]
              Rosenberg, J. and C. F. Jennings, "Framework, Use Cases
              and Requirements for AI Agent Protocols", Work in
              Progress, Internet-Draft, draft-rosenberg-ai-protocols-00,
              5 May 2025, <https://datatracker.ietf.org/doc/html/draft-
              rosenberg-ai-protocols-00>.

   [I-D.chuyi-nmrg-ai-agent-network]
              Guo, C., "Large Model based Agents for Network Operation
              and Maintenance", Work in Progress, Internet-Draft, draft-
              chuyi-nmrg-ai-agent-network-02, 20 October 2025,
              <https://datatracker.ietf.org/doc/html/draft-chuyi-nmrg-
              ai-agent-network-02>.

Authors' Addresses

   Mengyao Han (editor)
   China Unicom
   Beijing
   China
   Email: hanmy12@chinaunicom.cn

   Naihan Zhang (editor)
   China Unicom
   Beijing
   China
   Email: zhangnh12@chinaunicom.cn

   Jing Zhao (editor)
   China Unicom
   Beijing
   China
   Email: zhaoj501@chinaunicom.cn

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